A multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia

Abstract

This invention discloses a multi-channel intelligent humidification system for preventing delirium after general anesthesia. It includes multiple independent control modules for adjusting the humidity of the patient's respiratory tract, face, and surrounding environment. The system integrates a sensor network and adaptive control algorithm to perform closed-loop regulation of the humidification process based on the patient's physiological signals. The system adopts a modular design for easy assembly, disassembly, and operation. Clinical applications of this system include: reducing postoperative delirium-related risk factors by optimizing the local humidity environment, improving patient comfort, shortening postoperative recovery time, and can be used in conjunction with non-pharmacological interventions, which is expected to help reduce the incidence of postoperative delirium.

Description

Technical Field

[0001] This invention belongs to the field of medical system technology, specifically relating to a multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia. Background Technology

[0002] Postoperative delirium is a common neurological complication in patients after general anesthesia, with a significant epidemiological burden and clinical impact. Studies have shown that weather conditions such as decreased temperature, reduced sunshine hours, decreased atmospheric pressure, and increased humidity significantly reduce the risk of delirium in elderly patients living alone. Therefore, humidifying the inhaled gas for patients during the anesthesia recovery period has become an important supportive measure.

[0003] While humidification technology has its clinical value, existing strategies have several limitations when applied to the specific goal of preventing postoperative delirium. Specific technical bottlenecks include:

[0004] 1. Traditional humidification systems often rely on open-loop control of heating plate temperature or water flow rate, rather than direct feedback control of the humidity of the gas ultimately delivered to the patient.

[0005] 2. Even though some advanced systems have the ability to monitor flow and temperature, their control algorithms are still mainly designed to prevent hypothermia and airway dryness, and do not take delirium prevention as the core optimization goal.

[0006] 3. The user interface and interaction design of existing devices may be quite complex, requiring professional personnel to operate, which is not conducive to rapid deployment and adjustment.

[0007] In conclusion, the prevention of postoperative delirium urgently requires innovative environmental intervention strategies. Developing an intelligent humidification system that can achieve precise control, multi-channel adaptation, and easy integration into existing clinical workflows is expected to fill the current technological and clinical practice gaps and provide a new solution to reduce the incidence of this serious postoperative complication. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia, comprising: The respiratory humidification subsystem employs a respiratory humidity sensor and a flexible or compliant insulating sheet that fills the air gap between the heating element and the top heating plate. When the flow rate is lower than the minimum safe flow rate threshold, the heating power is reduced according to the flow rate difference to maintain the stability of the humidification output. The system is physically isolated from the heating element through a slot design and performs thermal cut-off when the heating power exceeds the threshold. The facial area humidification subsystem employs a facial skin humidity sensor and a micro-mist humidification submodule with an independent control loop. The facial skin humidity sensor monitors the skin moisture content and temperature of a specific area of ​​the face in a non-contact or micro-contact manner, adjusts the micro-mist humidification module according to the real-time skin humidity status, and introduces a temperature compensation mechanism to avoid facial skin temperature discomfort during the humidification process. The environmental microclimate control subsystem monitors the temperature and relative humidity of the local environment by distributing environmental temperature and humidity sensors at the patient's bedside, near the respiratory circuit, and around the bed. Based on the patient's core body temperature, sweating, and environmental monitoring data, it dynamically adjusts the temperature and humidity of the local area. The central coordinating controller, electrically connected to the humidification module, employs a fuzzy logic-based coordination strategy and adaptive algorithm to handle the coupling relationships and control conflicts between the respiratory tract humidification subsystem, the facial area humidification subsystem, and the environmental microclimate regulation subsystem. It integrates and controls each subsystem under preset control priorities. Based on graph theory and cooperative control theory, it calculates the optimal control command sequence by establishing a spatial relationship model of the states of each subsystem, and performs anomaly detection by executing a self-checking program based on deviation analysis of statistical process control.

[0009] Preferably, the minimum safe flow rate threshold is 1.5 L / min.

[0010] Preferably, the facial skin humidity sensor uses a nanoarray structure as the humidity-sensitive functional material, and combines a microheater and a thermistor. The microheater is used to maintain the sensor's operating temperature, and the thermistor is used to accurately monitor the temperature of the exhaled airflow to achieve temperature compensation.

[0011] Preferably, the respiratory tract humidification subsystem, the facial area humidification subsystem, and the environmental microclimate control subsystem all include a humidification chamber, a heating element, a water level sensor, and a temperature sensor; The built-in logic is as follows: By monitoring the current and temperature of the drive motor in real time, overload and overheating are prevented to protect the actuator; In respiratory circuit pressure monitoring, partial load and peak load patterns are analyzed, and when the pressure exceeds the safety threshold, it is reported to the control system for intervention and adjustment. Based on specific heat capacity analysis technology, the temperature response of the heating plate to a specific frequency excitation signal is analyzed to detect the low or no water state of the humidification chamber, thus ensuring humidification safety.

[0012] Preferably, each sensing node in the respiratory tract humidification subsystem, the facial area humidification subsystem, and the environmental microclimate control subsystem includes a microcontroller, a signal conditioning circuit, and a wireless communication module, wherein the wireless communication module uses Bluetooth or medical wireless frequency bands for data transmission.

[0013] Preferably, the control priority is to first meet the physiological humidification needs of the respiratory tract, then optimize facial comfort, and finally adjust the environmental microclimate under the premise of controllable overall energy consumption.

[0014] Preferably, the adaptive algorithm is fine-tuned based on the patient's basic information and parameters set by medical staff. It predicts the patient's real-time status by extracting features and analyzing patterns from respiratory humidity, temperature, and time-series data. An initial assessment is performed based on the patient's age, type of surgery, anesthetic drugs, and underlying disease-related factors, outputting target temperature and humidity setpoints and adjustment rate limits. A multi-objective optimization model is constructed and the objective function is solved. Combined with the patient's real-time status prediction, the humidification strategy is pre-adjusted and its parameters are dynamically optimized using temperature and humidity control loops, adapting the humidification strategy to the patient's real-time physiological changes. Simultaneously, based on the initial assessment results and continuous monitoring data, a personalized parameter model is established for the patient. Control signals are generated based on model analysis to drive the actuators for regulation.

[0015] Preferably, the temperature control loop uses distributed heating technology to configure multiple high-precision temperature sensors to monitor the ambient temperature, humidifier outlet temperature and patient interface temperature respectively, adjust the water / air contact area, and use the chamber outlet temperature and offset as the airway setpoint. According to changes in ambient temperature and the patient's physiological state, the heating power is dynamically adjusted within the adjustable range of offset to achieve precise temperature gradient control.

[0016] Preferably, the humidity control loop uses a humidity sensor and a dew point sensor. Through the principle of overcurrent correlation active humidification, absolute humidity is used as the main control parameter and relative humidity is used as the monitoring parameter to monitor absolute humidity and relative humidity in real time.

[0017] This invention provides a multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia. The system uses multi-regional independent control modules to humidify and regulate the patient's respiratory tract, face, and environmental microclimate to maintain a suitable local humidity environment. The system integrates an intelligent sensor network and an adaptive algorithm, which can dynamically adjust the humidification parameters according to the patient's physiological state, thereby providing a non-pharmacological intervention to reduce the incidence of postoperative delirium. Detailed Implementation

[0018] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0019] The prevention and management of post-anesthesia delirium (POD) in patients requires a balance of respiratory physiological protection, maintenance of facial comfort, and microclimate regulation in the recovery room. To achieve this goal, this invention provides a multi-channel intelligent humidification system for preventing post-anesthesia delirium. Its core architecture is based on the collaborative operation of multi-region independent control, an intelligent sensor network, and adaptive algorithms to accurately respond to the individualized needs of patients. The system includes: The humidification module features a quick-replaceable water purifier box design, supporting different capacity specifications to meet the needs of different patient groups, including a respiratory tract humidification subsystem, a facial area humidification subsystem, and an environmental microclimate control subsystem.

[0020] The respiratory humidification subsystem, which ensures airway moisture and maintains ciliary function, is based on the heating plate assembly of a medical humidifier system. It employs an improved thermal connection structure to form a respiratory humidity sensor. By introducing a flexible or compliant insulating sheet to fill the air gap between the heating element and the top heating plate, the heat conduction efficiency is significantly improved, ensuring stable humidification output even in low-flow or extremely low-flow treatment scenarios. The minimum safe flow threshold is 1.5 L / min. When the flow rate is detected to be below the threshold, the heating power is automatically reduced, and the flow recovery delay is a 30-second confirmation period.

[0021] The component also integrates safety features such as a thermal cut-off unit, and enhances system safety by physically isolating the heating element through a specially designed slot.

[0022] The respiratory humidity sensor collects the speed, temperature and absolute humidity of respiratory airflow in real time, which is used to analyze respiratory rate, tidal volume and respiratory waveform.

[0023] This facial humidification subsystem alleviates discomfort in the mouth and nose caused by dry air. It is equipped with an independent facial skin humidity sensor and a separate micro-mist humidification submodule with a control circuit. The facial skin humidity sensor monitors the skin moisture content and temperature of specific areas of the face (such as the perioral area and cheeks) through non-contact or micro-contact methods. It precisely adjusts the independent micro-mist humidification module according to the real-time humidity of the facial skin and introduces a temperature compensation mechanism to prevent facial skin temperature discomfort caused by the humidification process.

[0024] This subsystem works in conjunction with the respiratory subsystem, but each is controlled independently, avoiding the contradiction that a single humidifier source cannot meet both local and overall needs.

[0025] The high-precision humidity sensing unit in the facial skin humidity sensor employs a nanoarray structure as the humidity-sensitive functional material, combined with a microheater and a thermistor. The microheater maintains the sensor's operating temperature, improving sensitivity and accelerating response speed. The integrated thermistor accurately monitors the temperature of exhaled airflow, achieving effective temperature compensation and overcoming measurement errors caused by temperature drift in traditional sensors. Experimental data shows that the sensor achieves a sensitivity of 0.56 pF / %RH at 25°C and relative humidity above 60%. When the microheater raises the sensor surface temperature to 57.1°C, the sensitivity further increases to 3.24 pF / %RH, demonstrating excellent performance.

[0026] An environmental microclimate regulation subsystem creates and maintains a stable local microenvironment for patient beds. By distributing environmental temperature and humidity sensors at the head of the patient's bed, near the respiratory circuit, and around the bed, each bed is treated as an independent control unit. The overall balance is achieved through a central coordinator, which monitors changes in local temperature and relative humidity. It can dynamically adjust the temperature and humidity of the local area based on the patient's core body temperature, sweating, and environmental monitoring data, thereby supporting the core recommendations for environmental risk factor control in non-pharmacological interventions.

[0027] To ensure long-term reliability, specific performance indicators have been set for key sensors, as shown in Table 1. An automatic calibration mechanism has been built in, which periodically initiates the automatic calibration program. Using the built-in reference sensor and a controllable reference humidity source, each sensor is calibrated online to maintain the accuracy and stability of its long-term measurements.

[0028] Table 1 Key Sensor Performance Indicators

[0029] The respiratory humidification subsystem, facial area humidification subsystem, and environmental microclimate control subsystem are equipped with comprehensive safety logic. Actuator protection: Overload and overheating are prevented by real-time monitoring of the drive motor's current I and temperature t.

[0030] Barotrauma prevention: In breathing circuit pressure monitoring, analyze partial load and peak load patterns, and intervene immediately to adjust once the pressure exceeds the safety threshold.

[0031] Intelligent water shortage detection: Based on specific heat capacity analysis technology, the system analyzes the temperature response of the heating plate to a specific frequency excitation signal to reliably detect the low or insufficient water status of the humidification chamber, ensuring humidification safety.

[0032] The respiratory humidification subsystem, facial area humidification subsystem, and environmental microclimate regulation subsystem all include: Humidification chamber: medical-grade polycarbonate material, adjustable volume from 50-200ml.

[0033] Heating element: PTC ceramic heating element, power adjustable from 50-150W, response time <5 seconds.

[0034] Water level sensor: capacitive or optical water level detection, accuracy ±1ml.

[0035] Temperature sensor: NTC thermistor or PT100 platinum resistance thermometer, accuracy ±0.1°C.

[0036] And includes: Patient-side temperature and humidity sensor: placed at the connection point of the endotracheal tube or mask.

[0037] Ambient temperature and humidity sensors: placed in different areas of the anesthesia recovery room.

[0038] Flow sensor: Thermal mass flow meter, measuring range 0-100L / min, accuracy ±2%.

[0039] Gas composition sensor: Optional CO2 and O2 concentration monitoring modules.

[0040] The respiratory humidification subsystem, facial area humidification subsystem, and environmental microclimate control subsystem adopt a low-power wireless transmission design. Each sensing node consists of a microcontroller (MCU), signal conditioning circuit, or Bluetooth or medical wireless band transmission module. The MCU is responsible for acquiring the original analog signal, performing analog-to-digital conversion (ADC), and initial temperature drift compensation. The processed digital signal is wirelessly sent to the central coordination controller for further data fusion and algorithm analysis.

[0041] The central coordinating controller, based on an adaptive algorithm engine and powered by a microprocessor, is equipped with a main control chip, storage unit, and communication interface. It addresses the complex nonlinear coupling relationships between the three humidification channels—the respiratory tract humidification subsystem, the facial area humidification subsystem, and the environmental microclimate control subsystem—using a fuzzy logic-based coordination strategy. This strategy handles the coupling relationships and potential conflicts between the subsystems, organically integrating them under preset control priorities. This allows for flexible and smooth coordination of the subsystem outputs in a manner similar to that of a human expert, avoiding control conflicts and ensuring the achievement of overall control objectives. Furthermore, based on graph theory and cooperative control theory, by establishing a spatial relationship model of the states of each control channel, it can calculate the optimal control command sequence, achieving millisecond-level multi-channel synchronization. Finally, based on deviation analysis using statistical process control, it detects anomalies using a self-test program running at a frequency of 1Hz.

[0042] Control priorities: First, meet the physiological humidification needs of the respiratory tract (for example, if the respiratory tract humidity is low, increase the humidification power of the respiratory tract first), then optimize facial comfort, and finally, under the premise of controllable overall energy consumption, adjust the environmental microclimate to achieve the maximum balance between treatment effect and patient comfort.

[0043] The adaptive algorithm engine integrates respiratory system adaptive control theory with modern optimization methods. Based on basic patient information (such as age and ventilation mode), it automatically recommends initial parameters, allowing medical staff to fine-tune them according to patient comfort and clinical response. The system also integrates algorithms, extracting features and analyzing patterns from respiratory humidity, temperature, and time-series data to predict various patient states such as calm breathing, sighing, and apnea. Initial assessments are performed based on patient age, surgical type, anesthetic drugs, and underlying diseases, outputting target temperature and humidity setpoints and adjustment rate limits. By modeling a multi-objective optimization problem to solve the objective function, and combining the predicted states, the humidification strategy is pre-adjusted through independent temperature and humidity control loops. Parameters are dynamically optimized to balance treatment effectiveness, comfort, and energy consumption, enabling the humidification strategy to dynamically adapt to the patient's real-time physiological state and changes. Based on the initial assessment results and continuous monitoring data, a personalized parameter model is established for each patient. Control signals are generated based on parameter analysis to drive actuators for precise regulation, achieving a shift from a "one-size-fits-all" approach to a "tailor-made" approach. Specifically: Continuously monitor and analyze key parameters such as the pressure signal P(t) from the respiratory circuit of the respiratory humidification subsystem. By diagnosing the state of the respiratory system in real time (such as airway resistance and breathing mode), dynamically adjust the parameters of the control signal (such as the amplitude a of the useful signal). s The variance σ² ensures a high degree of synchronization between the equipment and the patient's respiratory activity, and promptly identifies abnormal patterns, providing a basis for subsequent personalized load adjustments (such as humidification intensity and airflow temperature).

[0044] The objective function is as follows:

[0045] In the formula, and These are the target humidity and temperature, respectively. and These are actual humidity and temperature measurements; This refers to the system's energy consumption.

[0046] The weighting coefficients α, β, and γ can be dynamically configured according to clinical priorities (e.g., patients at high risk of delirium require more emphasis on respiratory humidification).

[0047] For PACU, ICU and general wards, based on clinical workflow analysis, the operation links such as rapid start-up, patient switching and daily maintenance have been optimized to reduce the workload of medical staff. For various interference problems such as personnel movement, equipment start-up and shutdown, door and window opening and closing, the central coordination controller adopts a feedforward-feedback composite control structure to compensate in advance for measurable disturbances (such as sudden changes in ambient temperature), while the feedback control uses a high-gain observer to suppress unmeasurable disturbances to enhance robustness.

[0048] To address the potential increase in overall energy consumption due to independent control of multiple zones, the central coordinating controller employs dynamic programming to optimize energy consumption. By establishing precise thermodynamic and fluid dynamic models, it optimizes the working status and power distribution of each heating element in real time while meeting the patient's humidity and temperature requirements.

[0049] Temperature control loop: Utilizing distributed heating technology, multiple high-precision temperature sensors are configured to monitor ambient temperature, humidifier outlet temperature, and patient interface temperature. The water / air contact area is adjusted, and the chamber outlet temperature and offset (default 3°C) are used as the airway setpoint. Based on changes in ambient temperature and the patient's physiological state, the heating power is dynamically adjusted within the adjustable offset range (2-5°C) to achieve precise temperature gradient control. According to clinical guidelines, for patients requiring invasive ventilation, the goal is to maintain a gas temperature of approximately 37°C at the Y-connector of the patient's airway to simulate physiological conditions and prevent condensation.

[0050] Humidity control loop: Using humidity and dew point sensors, and through the active humidification principle such as flow-through, the absolute humidity (AH) (the actual water vapor content in a unit volume of gas, mg / L) is used as the main control parameter, and the relative humidity (RH) is used as the monitoring parameter. The absolute humidity and relative humidity are monitored in real time. For patients with invasive ventilation, the target absolute humidity is 44 mg / L (corresponding to 100% relative humidity at 37°C). For non-invasive ventilation or high-flow nasal cannula treatment, the target is adjusted accordingly.

[0051] The temperature gradient is precisely controlled as follows: when the actual offset is consistently below 2°C, the chamber outlet temperature will be gradually reduced (by 0.5°C each time, down to a minimum of 35.5°C) until the required catheter offset can be maintained.

[0052] The dual-closed-loop system based on the PID algorithm achieves coordinated control through a cross-feedback mechanism: Sampling frequency: 100Hz.

[0053] Control cycle: 10ms.

[0054] Implementation formula:

[0055] In the formula, It is a moment Controller output value; It is a proportional element that outputs a control quantity proportional to the current error; It is the calculus stage, which adjusts the output according to the error change trend, activates the damping effect, speeds up the response, and is used to eliminate steady-state error.

[0056] When the temperature changes, the humidity control system automatically adjusts the humidification amount to maintain the set absolute humidity; when the humidity changes, the temperature control system adjusts the heating power accordingly. By managing the temperature gradient, calculating the dew point temperature in real time, predicting the risk of condensation, and adjusting the heating strategy in advance, the system can prevent excessive condensation from forming in the circuit. This is a key consideration in clinical practice.

[0057] The central coordination controller stores a library of preset patterns: Standard invasive mode: suitable for patients with endotracheal intubation, target temperature 37°C, absolute humidity 44 mg / L.

[0058] Non-invasive / high-flow mode: Suitable for scenarios such as high-flow nasal oxygen therapy. The target temperature is usually set at 30-34°C, and the absolute humidity is reduced by 15 accordingly.

[0059] Elderly / Pediatric Mode: Adjusts temperature and humidity parameters according to the physiological characteristics of specific population groups.

[0060] Cleaning mode: Cleaning is performed after the patient's condition is determined to be stable, and a usage report (energy consumption, total running time, abnormal event records) is automatically generated.

[0061] Mode A, suitable for patients recovering from routine anesthesia: Standard humidification mode Target humidity: 44 mg H2O / L (equivalent to 100% relative humidity at 37°C).

[0062] Target temperature: 37°C.

[0063] Control strategy: Maintain constant temperature and humidity.

[0064] Model B, suitable for high-risk patients (e.g., age > 65 years, with cognitive impairment): Delirium Prevention Model Target humidity: dynamically adjusted, initially 44 mg H2O / L, gradually reduced according to the recovery stage.

[0065] Target temperature: Gradient control, gradually decreasing from 37°C to 32°C.

[0066] Special function: Combined with ambient light regulation, it simulates the natural circadian rhythm to promote sleep and reorientation, which is one of the key non-pharmacological interventions for preventing delirium.

[0067] Mode C of adaptive control based on real-time monitoring data: Personalized adjustment mode Algorithm inputs: bispectral index of EEG, heart rate variability, and respiratory pattern.

[0068] Control output: Dynamic optimization of temperature and humidity setpoints.

[0069] Learning mechanism: parameter tuning based on reinforcement learning.

[0070] The central coordinating controller stores a preset microenvironment library, as shown in Table 2: Table 2 Preset Microenvironment Library

[0071] The central control unit stores temperature safety protection, humidity safety protection, and alarm system classifications. Details are as follows: Temperature safety protection: High temperature limit: When the outlet temperature exceeds the safety threshold, heating will be immediately cut off and an alarm will be triggered.

[0072] Gradient control: Supports positive, negative, or zero gradient settings to minimize the risk of condensation in the breathing circuit while providing sufficient humidity, which is important for preventing complications such as ventilator-associated pneumonia.

[0073] Overheat protection: Equipped with multiple temperature protection circuits.

[0074] Humidity safety protection: Over-humidity and under-humidity protection: Monitor and prevent humidity from being too high or too low.

[0075] Water level monitoring: Monitors the water level in the water purification box and provides early warning when the water level is low to prevent the equipment from burning dry.

[0076] Alarm system classification: The alarm system classifies events according to their severity and provides multiple prompts, including visual and auditory ones, to ensure that abnormal situations can be identified and handled in a timely manner.

[0077] In summary, the technical architecture integrating multi-regional independent control, intelligent sensor networks, and advanced adaptive algorithms provides a precise, dynamic, and safe humidification support strategy for patients after general anesthesia, offering innovative hardware and software solutions to reduce the risk of postoperative delirium and improve patient recovery quality.

[0078] The patient interface module uses a magnetic quick-connect interface, supports one-handed operation, has a short connection time, and features a fault-proof design to ensure that each module can only be connected in the correct direction to avoid clinical operation errors. All electrical interfaces are waterproof and dustproof to ensure reliability and safety in the clinical environment, and provide a variety of adapter options, including nasal cannula interface, mask adapter, and tracheostomy interface, to achieve seamless integration with existing respiratory therapy equipment.

[0079] The user-friendly interface uses a capacitive touchscreen, supporting multi-touch and gesture operation. The interface design adheres to human-computer interaction standards for medical devices and has the following characteristics: Hierarchical menu structure: The first-level menu displays key parameters (temperature, humidity, flow rate), while the second-level menu provides detailed setting options.

[0080] Color coding system: Different parameter ranges are identified by different colors, which facilitates quick identification of abnormal states.

[0081] Voice prompt function: Supports confirmation of key operations and alarm prompts, reducing reliance on visual input.

[0082] Night mode: Automatically reduces screen brightness and switches to a dark theme to minimize disruption to the patient's sleep.

[0083] Touchscreen display: 7-10 inch color LCD with a resolution of no less than 1024×768.

[0084] Physical control knob: for manual adjustment in emergency situations.

[0085] Audible and visual alarm system: multi-level alarm indicators (visual LED + audible buzzer).

[0086] A multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia features a lightweight design, an adjustable height mobile support with rotation and locking functions, and medical casters and braking devices at the bottom of the support for easy and rapid movement between different wards. The outer shell has an antibacterial coating, and the seams are sealed to reduce the risk of cross-infection and facilitate cleaning. It strictly adheres to the safety and performance standards for medical electrical equipment, including requirements for electrical safety, electromagnetic compatibility, and biocompatibility.

[0087] A multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia: long-term efficacy evaluation and quality improvement are as follows: 1. To ensure the long-term value of the technology, a multi-dimensional evaluation indicator system should be established, covering multiple dimensions such as clinical, economic, user experience, and system efficiency: Clinical outcome indicators include the incidence, duration, severity, speed of cognitive function recovery, and incidence of complications.

[0088] Economic benefit indicators: total hospitalization costs, return on equipment investment, savings in nursing hours, and patient readmission rate.

[0089] Patient and family experience indicators: Comfort, anxiety level and overall hospitalization experience were assessed using a satisfaction scale.

[0090] System efficiency indicators: bed turnover rate, equipment uptime, and integration with other hospital information systems.

[0091] 2. Establish a continuous quality improvement mechanism: Real-time data monitoring and analysis: Utilizing the device's built-in sensors and connectivity, it continuously collects environmental parameters and patient outcome data for dynamic analysis.

[0092] Closed-loop feedback mechanism: Regularly collect usage feedback from medical staff and experience reports from patients as important inputs for technology iteration and process optimization.

[0093] Evidence-based technology iteration: problems and needs discovered in clinical practice are fed back to the R&D end, driving the continuous upgrading of humidification equipment and control algorithms.

[0094] Conduct multi-center clinical studies: Collaborate with multiple medical institutions to conduct long-term, large-scale, and effective studies to further verify the universality and cost-effectiveness of the technology.

[0095] 3. Promote standardization. Establish technical operating procedures: clearly define the standard procedures for equipment installation, daily use, maintenance, and data interpretation.

[0096] Establish a training and certification system: Develop standardized training courses and assessment mechanisms to ensure that medical staff can use the technology correctly and effectively.

[0097] Unified performance evaluation standards: Define core evaluation indicators and data collection methods to facilitate the comparison and integration of data among different institutions.

[0098] Standardize data interfaces and security: Develop standards for data interaction between medical devices to ensure patient privacy and data security.

[0099] This invention provides a multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia. It can be integrated with early activity programs to ensure that the air patients breathe during activity remains comfortable and humid. It can also be coordinated with pain management, as physical discomfort (including respiratory dryness) can exacerbate pain perception and anxiety, while a comfortable environment helps to relax the mind and body. As part of standard care, it can assist in reorientation (such as reminding patients that they are in a comfortable and caring environment with appropriate temperature and humidity). This integrated approach conforms to the "multidisciplinary non-pharmacological management" principle advocated by guidelines. It standardizes and refines a specific environmental intervention through technical means, making up for the shortcomings of traditional methods where humidity control is often neglected or difficult to implement individually.

[0100] This invention provides a multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia, with the following protection points: 1. Multi-channel independent control architecture: It realizes the coordinated and independent optimization of the patient's breathing circuit, local body surface environment and overall ward environment, breaking through the limitations of traditional single-point humidification equipment, and can comprehensively cope with the impact of environmental factors on delirium risk.

[0101] 2. Intelligent decision-making algorithm: It combines individual patient risk factors (such as age and cognitive status), surgical type and real-time physiological data to achieve a leap from fixed parameter settings to personalized and dynamic adjustment.

[0102] 3. Integrated non-pharmacological intervention: By combining precise humidification and temperature control of respiratory gases with environmental light and noise management, a multi-sensory and comprehensive non-pharmacological intervention program is formed, which is highly consistent with the core idea of ​​multidisciplinary non-pharmacological intervention emphasized in the postoperative delirium management guidelines.

[0103] 4. Safety Redundancy Design: A multi-layered safety system has been constructed, from hardware protection and software monitoring to clinical operation procedures, to ensure patient safety in complex clinical environments.

[0104] 5. Deep integration of clinical data: The system design supports seamless integration with hospital information systems, which not only facilitates equipment management but also provides high-quality data support for clinical research and evidence-based medicine decision-making.

[0105] This invention provides a multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia, which is expected to bring the following clinical benefits: Strengthening delirium prevention: As a key component of multimodal intervention, it is expected to further reduce the incidence and severity of delirium in high-risk patients, especially the elderly and those with cognitive impairment.

[0106] Improve patients' subjective experience: Significantly improve patients' comfort scores during the recovery period and early postoperative period, and reduce common complaints such as dry mouth, sore throat, and respiratory irritation.

[0107] Reduce related complications: By protecting the airway and maintaining body temperature, the risk of postoperative pulmonary infection, atelectasis, and hypothermia-related cardiovascular events may be indirectly reduced.

[0108] Improve nursing efficiency: The modular, multi-channel design facilitates rapid deployment and unified management in different wards (such as PACU, ICU, and general wards), enabling environmental control to shift from experience-based judgment to data-driven precision nursing.

[0109] In summary, this system transforms environmental humidity from a passive background factor into an active, precisely controllable tool for delirium prevention. Its clinical advantages are rooted in the understanding of environmental stress factors in the pathophysiology of post-diarrhea and, through technological innovation, achieve deep integration with existing best practice guidelines, providing a practical approach to improving postoperative brain health.

[0110] The direction of technological iteration is as follows: 1. Development will move towards multi-parameter integrated monitoring and intelligent control. For example, it can integrate sensors for multiple parameters such as humidity, temperature, CO2 concentration, and light intensity to achieve comprehensive monitoring of environmental parameters. Simultaneously, it will draw on the modular design and wide-flow-range (2-60L / min) precision control technology of existing medical equipment, combined with intelligent algorithms, to analyze the correlation between individual patient characteristics and environmental parameters, enabling personalized control. The ultimate goal is to build an integrated IoT platform to achieve centralized monitoring and intelligent management of the hospital's multi-area environment.

[0111] 2. Focus on establishing individualized risk assessment models, taking into account factors such as patient age, underlying diseases, and cognitive status, to dynamically adjust humidification strategies. Through wearable devices and remote monitoring systems, continuous monitoring and intervention can be achieved from the hospital to the home environment, compensating for the deficiencies in temperature and humidity control in the home environment.

[0112] The possibilities for expanding clinical applications are as follows: 1. Expanded applicable population: elderly surgical patients, ICU patients, neurosurgical patients, and pediatric patients undergoing high-risk surgeries such as hip fractures and heart surgery.

[0113] 2. Diversification of application scenarios: Comprehensive management throughout the entire surgical period: closed-loop environmental management of the entire process from preoperative preparation, intraoperative maintenance to postoperative recovery.

[0114] Multi-departmental collaborative application: Jointly deployed in multiple departments such as anesthesiology, ICU, geriatrics, orthopedics, and neurosurgery.

[0115] Continuous management inside and outside the hospital: Supporting patients' smooth transition from the operating room, post-anesthesia care unit (PACU), general ward to home rehabilitation environment.

[0116] Specialized applications: Suitable for medical environments with different requirements for temperature and humidity, such as operating rooms, recovery rooms, and isolation wards.

[0117] 3. Humidification strategies should be used in conjunction with the following measures: Drug intervention: Used in combination with dexmedetomidine, melatonin, and other drugs that have shown potential preventive effects.

[0118] Non-pharmacological interventions: Combined with standard nursing interventions such as cognitive training, early mobilization, and pain management.

[0119] Nursing intervention: as an auxiliary tool for nursing processes such as orientation training and family involvement.

[0120] Economic Benefit Analysis and Market Prospects: 1. Direct Cost-Benefit Analysis: The direct economic benefits of environmental interventions have been supported by data. These economic benefits are specifically reflected in: Reduced hospitalization costs: Shorter hospital stays directly reduce bed fees, nursing fees, and overall treatment costs.

[0121] Medical resource conservation: Reduce the need for prolonged mechanical ventilation, special monitoring, and one-on-one care due to delirium.

[0122] Reduced complication costs: Prevents secondary complications such as falls, unplanned extubation, and infections caused by delirium, avoiding additional examination and treatment expenses.

[0123] 2. Indirect economic benefits: Improved quality of life for patients: Reduced cognitive impairment, accelerated recovery process, and improved long-term quality of life.

[0124] Reduced burden on families and society: Shortens the time family members spend accompanying patients, reducing the economic pressure on families and social security expenditures caused by long-term hospitalization or disability of patients.

[0125] Improving the efficiency of the healthcare system: accelerating bed turnover, increasing the utilization rate of medical resources, and benefiting more patients.

[0126] 3. Market Acceptance Assessment: The market promotion of this technology will be influenced by multiple factors, presenting both opportunities and challenges.

Claims

1. A multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia, characterized in that, include: The respiratory humidification subsystem employs a respiratory humidity sensor and a flexible or compliant insulating sheet that fills the air gap between the heating element and the top heating plate. When the flow rate is lower than the minimum safe flow rate threshold, the heating power is reduced according to the flow rate difference to maintain the stability of the humidification output. The system is physically isolated from the heating element through a slot design and performs thermal cut-off when the heating power exceeds the threshold. The facial area humidification subsystem employs a facial skin humidity sensor and a micro-mist humidification submodule with an independent control loop. The facial skin humidity sensor monitors the skin moisture content and temperature of a specific area of ​​the face in a non-contact or micro-contact manner, adjusts the micro-mist humidification module according to the real-time skin humidity status, and introduces a temperature compensation mechanism to avoid facial skin temperature discomfort during the humidification process. The environmental microclimate control subsystem monitors the temperature and relative humidity of the local environment by distributing environmental temperature and humidity sensors at the patient's bedside, near the respiratory circuit, and around the bed. Based on the patient's core body temperature, sweating, and environmental monitoring data, it dynamically adjusts the temperature and humidity of the local area. The central coordinating controller, electrically connected to the humidification module, employs a fuzzy logic-based coordination strategy and adaptive algorithm to handle the coupling relationships and control conflicts between the respiratory tract humidification subsystem, the facial area humidification subsystem, and the environmental microclimate regulation subsystem. It integrates and controls each subsystem under preset control priorities. Based on graph theory and cooperative control theory, it calculates the optimal control command sequence by establishing a spatial relationship model of the states of each subsystem, and performs anomaly detection by executing a self-checking program based on deviation analysis of statistical process control.

2. The multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia as described in claim 1, characterized in that, The minimum safe flow rate threshold is 1.5 L / min.

3. The multi-channel intelligent humidification system for preventing delirium after general anesthesia as described in claim 1, characterized in that, The facial skin humidity sensor uses a nanoarray structure as the humidity-sensitive functional material, combined with a microheater and a thermistor. The microheater is used to maintain the sensor's operating temperature, and the thermistor is used to accurately monitor the temperature of the exhaled airflow to achieve temperature compensation.

4. The multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia as described in claim 1, characterized in that, The respiratory tract humidification subsystem, facial area humidification subsystem, and environmental microclimate control subsystem all include a humidification chamber, a heating element, a water level sensor, and a temperature sensor. The built-in logic is as follows: By monitoring the current and temperature of the drive motor in real time, overload and overheating are prevented to protect the actuator; In respiratory circuit pressure monitoring, partial load and peak load patterns are analyzed, and when the pressure exceeds the safety threshold, it is reported to the control system for intervention and adjustment. Based on specific heat capacity analysis technology, the temperature response of the heating plate to a specific frequency excitation signal is analyzed to detect the low or no water state of the humidification chamber, thus ensuring humidification safety.

5. The multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia as described in claim 1, characterized in that, Each sensing node in the respiratory tract humidification subsystem, facial area humidification subsystem, and environmental microclimate control subsystem includes a microcontroller, signal conditioning circuitry, and a wireless communication module. The wireless communication module uses Bluetooth or medical wireless frequency bands for data transmission.

6. The multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia as described in claim 1, characterized in that, The control priority is to first meet the physiological humidification needs of the respiratory tract, then optimize facial comfort, and finally adjust the environmental microclimate under the premise of controllable overall energy consumption.

7. A multi-channel humidification system for preventing delirium in patients after general anesthesia as described in claim 1, characterized in that, The adaptive algorithm is fine-tuned based on the patient's basic information and parameters set by medical staff. It predicts the patient's real-time status by extracting features and analyzing patterns from respiratory humidity, temperature, and time-series data. It performs an initial assessment based on the patient's age, type of surgery, anesthetic drugs, and underlying disease-related factors, outputting target temperature and humidity setpoints and adjustment rate limits. By constructing a multi-objective optimization model and solving the objective function, combined with the patient's real-time status prediction, it uses temperature and humidity control loops to pre-adjust and dynamically optimize the humidification strategy, making the humidification strategy adapt to the patient's real-time physiological changes. At the same time, based on the initial assessment results and continuous monitoring data, it establishes a personalized parameter model for the patient, generates control signals based on model analysis, and drives the actuators to perform regulation.

8. The multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia as described in claim 7, characterized in that, The temperature control loop employs distributed heating technology with multiple high-precision temperature sensors to monitor ambient temperature, humidifier outlet temperature, and patient interface temperature. It adjusts the water / air contact area, uses the chamber outlet temperature and offset as the airway setpoint, and dynamically adjusts the heating power within the adjustable offset range based on changes in ambient temperature and the patient's physiological state to achieve precise temperature gradient control.

9. A multi-channel intelligent humidification system for preventing delirium in patients after general anesthesia as described in claim 7, characterized in that, The humidity control loop uses a humidity sensor and a dew point sensor. Through the principle of overflow-correlated active humidification, it uses absolute humidity as the main control parameter and relative humidity as the monitoring parameter to monitor absolute humidity and relative humidity in real time.

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